DBMS > Bangdb vs. Lovefield vs. SiriDB vs. Solr vs. SWC-DB

System Properties Comparison Bangdb vs. Lovefield vs. SiriDB vs. Solr vs. SWC-DB

Editorial information provided by DB-Engines

Name

Description

Converged and high performance database for device data, events, time series, document and graph

Embeddable relational database for web apps written in pure JavaScript

Open Source Time Series DBMS

A widely used distributed, scalable search engine based on Apache Lucene

A high performance, scalable Wide Column DBMS

Primary database model

Document store
Graph DBMS
Time Series DBMS

Secondary database models

Spatial DBMS

Time Series DBMS

DB-Engines Ranking measures the popularity of database management systems
Trend Chart

Score	0.16
Rank	#338	Overall
	#47	Document stores
	#32	Graph DBMS
	#31	Time Series DBMS

Score	0.33
Rank	#286	Overall
	#131	Relational DBMS

Score	0.07
Rank	#378	Overall
	#42	Time Series DBMS

Score	41.02
Rank	#24	Overall
	#3	Search engines

Score	0.08
Rank	#364	Overall
	#13	Wide column stores

Website

bangdb.com

google.github.io/lovefield

siridb.com

solr.apache.org

github.com/kashirin-alex/swc-db
www.swcdb.org

Technical documentation

docs.bangdb.com

github.com/google/lovefield/blob/master/docs/spec_index.md

docs.siridb.com

solr.apache.org/resources.html

Developer

Sachin Sinha, BangDB

Google

Cesbit

Apache Software Foundation

Alex Kashirin

Initial release

2012

2014

2017

2006

2020

Current release

BangDB 2.0, October 2021

2.1.12, February 2017

9.6.1, May 2024

0.5, April 2021

License

Commercial or Open Source

Open Source

BSD 3

Open Source

Apache 2.0

Open Source

MIT License

Open Source

Apache Version 2

Open Source

GPL V3

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

C, C++

JavaScript

Java

C++

Server operating systems

Linux

server-less, requires a JavaScript environment (browser, Node.js)

tested with Chrome, Firefox, IE, Safari

Linux

All OS with a Java VM

runs as a servlet in servlet container (e.g. Tomcat, Jetty is included)

Linux

Data scheme

schema-free

yes

Dynamic Fields enables on-the-fly addition of new fields

schema-free

Typing

predefined data types such as float or date

yes: string, long, double, int, geospatial, stream, events

yes

Numeric data

yes

supports customizable data types and automatic typing

XML support

Some form of processing data in XML format, e.g. support for XML data structures, and/or support for XPath, XQuery or XSLT.

yes

Secondary indexes

yes

secondary, composite, nested, reverse, geospatial

yes

All search fields are automatically indexed

SQL

Support of SQL

SQL like support with command line tool

SQL-like query language

via JavaScript builder pattern

Solr Parallel SQL Interface

SQL-like query language

APIs and other access methods

Proprietary protocol
RESTful HTTP API

HTTP API

Java API
RESTful HTTP/JSON API

Proprietary protocol
Thrift

Supported programming languages

C
C#
C++
Java
Python

JavaScript

C
C++
Go
Java
JavaScript (Node.js)
PHP
Python
R

.Net
Erlang
Java
JavaScript
any language that supports sockets and either XML or JSON
Perl
PHP
Python
Ruby
Scala

C++

Server-side scripts

Stored procedures

Java plugins

Triggers

yes, Notifications (with Streaming only)

Using read-only observers

yes

User configurable commands triggered on index changes

Partitioning methods

Methods for storing different data on different nodes

Sharding (enterprise version only). P2P based virtual network overlay with consistent hashing and chord algorithm

none

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor, Knob for CAP (enterprise version only)

none

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

spark-solr: github.com/lucidworks/spark-solr and streaming expressions to reduce

Consistency concepts

Methods to ensure consistency in a distributed system

Tunable consistency, set CAP knob accordingly

Eventual Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

Support to ensure data integrity after non-atomic manipulations of data

ACID

optimistic locking

Concurrency

Support for concurrent manipulation of data

yes, optimistic concurrency control

yes

Durability

Support for making data persistent

yes, implements WAL (Write ahead log) as well

yes, by using IndexedDB or the cloud service Firebase Realtime Database

yes

In-memory capabilities

Is there an option to define some or all structures to be held in-memory only.

yes, run db with in-memory only mode

yes

using MemoryDB

yes

User concepts

Access control

yes (enterprise version only)

simple rights management via user accounts

yes

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